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Based on FTA Intelligent Construction Equipment Reliability Analysis System Research

机译:基于FTA智能施工设备可靠性分析系统研究

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The running status of intelligent construction electric power supply, the elevator, the illumination control,for draining water, the fire prevention, warmly passes equipment the air conditioning and so on, which is in close correlation with people's work life, Once breaking down, will be able to create the huge economic loss and the serious social influence. The traditional solution is carded on the real-time monitor, the implementation diagnosis to the system,when the fault appears, starting the emergency system or the emergency apparatus. How to realize the intelligent construction equipment implementation failure predication and the synthesis management, even on forecast in the intelligent construction design process the system reliability weak link, enable in the building intellectualization project always not to have the very good solution question.This paper introduces the Fault Tree Analytic method (FAT) in intelligence construction reliability analysis, through the analysis on each kind of factor which may create the intelligent construction system equipment fault, establishing the fault tree model, determining system fault reasons in each possibility combination way and its probability, computing system fault probability, to enhance the system reliability. This article has developed the FTA use domain, also helps the personnel to discover the essential unit in the building design stage to design, and provides the certain advisory opinion to designs, may help the people carry on the fault diagnosis and the forecast in the using service stage.
机译:智能建筑电力供应的运行状态,电梯,照明控制,防水,防火,热情地通过设备空调等,这与人们的工作生活密切相关,一旦崩溃,就会能够创造巨大的经济损失和严重的社会影响力。传统的解决方案在实时监视器上梳理,对系统的实现诊断,当故障出现时,启动紧急系统或应急设备。如何实现智能施工设备实现失败预测和综合管理,即使在智能施工设计过程中的预测中的系统可靠性弱链路,在建筑物智能化项目中的启用总是不具有非常好的解决方案问题。本文介绍了故障树分析方法(脂肪)智能施工可靠性分析,通过分析各种因素,可以创建智能施工系统设备故障,建立故障树模型,确定各种可能性组合方式的系统故障原因及其概率,计算系统故障概率,提高系统可靠性。本文开发了FTA使用域,还可以帮助人员发现建筑设计阶段的基本单位设计,并为设计提供某些咨询意见,可以帮助人们进行故障诊断和预测服务阶段。

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